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Power Virtual Agents brings improvements in the authoring experience with commenting, Power Pages integration, data loss prevention options, proactive bot update messaging in Microsoft Teams, and more. Creating a bot is typically a complex and time-intensive process, requiring long content update cycles and a team of experts. Power Virtual Agents gives anyone in your organization the ability to create powerful custom bots using an easy, code-free graphical interface, without the need for AI experts, data scientists, or teams of developers. A bot can interact with users, ask for clarifying information, and ultimately answer a user's questions.
Data integration will deliver various big data performance and high scale improvements to connectivity scenarios from Azure Synapse, Dataverse, Snowflake, Databricks, Google BigQuery, and Amazon Redshift, as well as many other Power Query connectors. Several enhancements, like additional security roles, logs and diagnostics for VNet will also be included in this wave. Within the Power Query Editor and transformation experience, you can expect new transforms and capabilities to simplify your visual data prep in a visual authoring environment. We're also releasing significant improvements to offer a new generation of Dataflows to Microsoft Data Cloud, supporting Power Platform and beyond. Finally, we're excited to share a new data integration story empowering aligned data integration scenarios to Microsoft Data Cloud across both citizen and professional developers.
This paper proposes and evaluates an approach to alleviate the limitations preventing fully exploiting digital clinical pathology for training-assisted diagnosis tools. The proposed approach includes a Natural Language Processing (NLP) pipeline to automatically analyze free-text reports and a computer vision algorithm trained with weak annotations to classify images. The NLP pipeline automatically extracts semantically meaningful concepts from free-text diagnosis reports to be used as weak labels for training an image classifier. The implementation of the approach can be changed and modified, allowing to adopt different techniques that vary depending on the characteristics of the problem to solve and on the state-of-the-art algorithm advancement. The approach is tested on digital pathology colon data, completely bypassing the need for human and unleashing the potential of data acquired in clinical workflows. To demonstrate the reliability of automatically generated weak labels for training, the image classifier is compared with the same image classifier architecture, trained using manual weak labels.
The first consequence is a potential breakthrough in the digital pathology domain. Since it is possible to overcome the need for human intervention to annotate images and reports, it is also possible to exploit exascale datasets coming from heterogeneous pathology workflows, unleashing the full potential of digital pathology. The fact that the presented approach does not need any human annotation removes all the constraints related to data annotation when free-text diagnostic reports are available, allowing the collection of massive clinical datasets (including hundreds of thousand WSIs) for training computer-aided diagnosis systems on a variety of concepts presented in routine reports. Data processing without curation shows that it is possible to exploit data from several centers, overcoming the limitations of standardization in image format, text report format, and image processing systems. Increasing the number of centers can improve the performance of the algorithms in terms of capability to deal with image heterogeneity, allowing researchers to collect big datasets to train robust tools with limited effort and triggering a virtuous circle in the computational pathology domain. 076b4e4f54